Recently, some works [1]}, [2]}, [3]}, [4]} have used larger high-resolution patches to improve the segmentation of histology images. Zanjani et al. [3]} and Li et al. [4]} used a CNN based feature extractor followed by a Conditional Random Field (CRF) for context learning. Latter is end-to-end trainable with a patch size of \(672\times 672\) , considerably smaller than the patch size used in our proposed method. Agarawalla et al.[1]} and Kong et al. [2]} used a 2D-LSTM instead of CRF to improve tumor segmentation. Some works [9]}, [10]} used larger patches at high resolution for the task of context-based classification. Koohbanani et al.[9]} proposed a context-aware network for breast cancer classification. They used standard SVM to learn the context from the CNN based features of the patches extracted from a high-resolution image. Due to the nature of the final classifier, this work is only capable of capturing a limited context. Bejnordi et al. [10]} proposed a similar approach for breast tissue classification. They trained their network in two steps. In the first step, they used a small patch size and in the second step, they fixed the weights of half of the network to feed a larger patch for training the remaining half of the network. Their network also suffers from a limited context problem as they managed to train a network with the largest patch size of \(1,024\times 1,024\)  pixels with small batch size (10 patches). Sirinukunwattana et al. [13]} presented a systematic comparison of different context-aware methods to highlight the importance of context-aware learning.
